The first step is to detect the objects in the input image. In general, any state-of-the-art object detector can be used, such as YOLO [1]}, SSD [2]} or Faster R-CNN [3]}. Instead of using these top-down methods that are built on bounding boxes, we resort to PoseCNN [4]}. PoseCNN first labels every pixel into an object class and then applies Hough voting to find the centers and bounding boxes of the objects. PoseCNN generates segmentation masks of the objects which can be used to segment the point clouds of the objects in pose refinement [5]}. In addition, the object centers from PoseCNN are more accurate than bounding box centers from the top-down methods thanks to the Hough voting strategy.
